检索结果-内蒙古大学图书馆

MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption

引用

Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Continuous Cognitive Diagnosis with Fuzzy Strategy-Based Hybrid Genetic Algorithm 3rd

Enhancing Continuous Cognitive Diagnosis with Fuzzy Strateg...

引用

3rd International Conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： He, Chenlong Hu, Xuegang Cao, Zhiyong Bu, Chenyang Luo, Wenjian Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819645053

Continuous cognitive diagnosis models (CDMs) are vital tools for assessing students’ mastery of knowledge points. However, traditional probability-based CDMs are prone to falling into local optima due to their use of single-point search methods, which can affect the accuracy of the models. To address this issue, we propose a hybrid genetic algorithm (HGA) enhanced with a fuzzy strategy to improve continuous cognitive diagnosis. This approach introduces the multidimensional item response theory (MIRT) as a local search operator to boost diagnostic precision. Additionally, considering the limitation on the number of local searches within a finite time, we introduce a fuzzy strategy that dynamically adjusts the number of local searches by evaluating the similarity between the current population and the elite set, thus balancing global and local search. Experimental results on three real-world datasets demonstrate that our method significantly outperforms six existing comparison models, validating the effectiveness of the fuzzy strategy and continuous CDM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Continuous cognitive diagnosis educational data mining Evolutionary algorithm Local search

来源：评论

学校读者我要写书评

暂无评论

Disentangled Noisy Correspondence Learning

引用

IEEE Transactions on Image Processing 2025年 34卷 2602-2615页

作者： Dang, Zhuohang Luo, Minnan Wang, Jihong Jia, Chengyou Han, Haochen Wan, Herun Dai, Guang Chang, Xiaojun Wang, Jingdong Xi’an Jiaotong University School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi’an710049 China SGIT AI Laboratory Xi’an710048 China State Grid Corporation of China State Grid Shaanxi Electric Power Company Ltd. Xi’an710048 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Abu Dhabi United Arab Emirates Baidu Inc. Beijing100085 China

Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impractical as real-world data inevitably involves imperfect alignments, i.e., noisy correspondences. Although some works explore similarity-based strategies to address such noise, they suffer from sub-optimal similarity predictions influenced by modality-exclusive information (MEI), e.g., background noise in images and abstract definitions in texts. This issue arises as MEI is not shared across modalities, thus aligning it in training can markedly mislead similarity predictions. Moreover, although intuitive, directly applying previous cross-modal disentanglement methods suffers from limited noise tolerance and disentanglement efficacy. Inspired by the robustness of information bottlenecks against noise, we introduce DisNCL, a novel information-theoretic framework for feature Disentanglement in Noisy Correspondence Learning, to adaptively balance the extraction of modality-invariant information (MII) and MEI with certifiable optimal cross-modal disentanglement efficacy. DisNCL then enhances similarity predictions in modality-invariant subspace, thereby greatly boosting similarity-based alleviation strategy for noisy correspondences. Furthermore, DisNCL introduces soft matching targets to model noisy many-to-many relationships inherent in multi-modal inputs for noise-robust and accurate cross-modal alignment. Extensive experiments confirm DisNCL’s efficacy by 2% average recall improvement. Mutual information estimation and visualization results show that DisNCL learns meaningful MII/MEI subspaces, validating our theoretical analyses. © 1992-2012 IEEE.

关键词： Information theory

来源：评论

学校读者我要写书评

暂无评论

Electrochemical dissolution, reduction, and nucleation mechanisms of molybdenum in NaCl-KCl molten salt systems

引用

Journal of Materials Science and Technology 2025年 238卷 45-54页

作者： Lv, Hongzhan Zhang, Liwen Xi, Xiaoli Nie, Zuoren Collaborative Innovation Center of Capital Resource-Recycling Material Technology Beijing University of Technology Beijing100124 China Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China

This study investigates the anodic dissolution and electrochemical behavior of molybdenum in a NaCl-KCl molten salt system at 1023 K. The anodic dissolution process was systematically analyzed, revealing a sequential oxidation pathway of molybdenum into high-valence ions (Mo6+, Mo5+, Mo4+) under varying electrolysis potentials. Electrochemical Impedance Spectroscopy (EIS) demonstrated that the dissolution is governed by both charge transfer and diffusion mechanisms, with reduced impedance at higher potentials facilitating molybdenum dissolution. The reduction behavior of dissolved molybdenum ions was further explored using cyclic voltammetry (CV) and square wave voltammetry (SWV), confirming a multi-step reduction process controlled by diffusion and high reversibility. Nucleation studies using chronoamperometry established that molybdenum deposition follows an instantaneous nucleation mechanism. Morphological analysis of cathodic deposits revealed that current density significantly influences particle size, transitioning from nano-sized spherical particles to larger equiaxed crystals with increasing current density. These findings provide a comprehensive understanding of molybdenum's electrochemical properties in molten salts, offering valuable insights for optimizing electrolysis processes and advancing molybdenum-based material production. © 2025

关键词： Cyclic voltammetry

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning Based data-Driven Control Method for Dynamic Systems and Its Application to FLUIDMechatronix Platform 16th

Reinforcement Learning Based Data-Driven Control Method for...

引用

16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Zou, Zonghan Gao, Jingjing Yang, Xu Huang, Jian Zhang, Tao Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789819617760

With the increasing complexity of industrial process, accurate models of automatic control systems are difficult to be established, which makes model-based control methods become less effective. On this premise, a reinforcement learning (RL) based data-driven control method for dynamic systems is proposed in this paper. For our purposes, a model-free RL controller with an Actor-Critic structure is developed by using neural networks (NN), combining the merits of both value-based and policy-based RL algorithms. Policy of the actor is assessed by the critic, updating parameters of RL controller in real-time for online optimization. The effectiveness of the RL controller is demonstrated by an experimental study on FLUIDMechatronix platform. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Superradiant growth anomaly magnification in evolution of vector bosonic condensates bounded by a Kerr black hole with near-horizon reflection

引用

Science china(Physics,Mechanics & Astronomy) 2025年第4期68卷 62-81页

作者： Nayun Jia Yin-Da Guo Gui-Rong Liang Zhan-Feng Mai Xin Zhang Department of Physics Southern University of Science and TechnologyShenzhen 518055China Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Institute of Frontier and Interdisciplinary Science Key Laboratory of Particle Physics and Particle Irradiation(Ministry of Education)Shandong UniversityQingdao 266237China School of Materials Science and Physics China University of Mining and TechnologyXuzhou 221116China Kavli Institute for Astronomy and Astrophysics Peking UniversityBeijing 100871China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

Ultralight vector particles can form evolving condensates around a Kerr black hole(BH)due to superradiant *** study the effect of near-horizon reflection on the evolution of this system:by matching three pieces of asymptotic expansions of the Proca equation in Kerr metric and considering the leading order in the electric mode,we present explicit analytical expressions for the corrected spectrum and the superradiant instability ***,in high-spin BH cases,we identify an anomalous situation where the superradiance rate is temporarily increased by the reflection parameter R,which also occurs in the scalar scenario,but is largely magnified in vector condensates due to a faster growth rate in dominant *** point out that the condition for the growth anomaly in the adiabatic case is that information carried per particle exceeds a certain value δ1/δN>2πκ_(B)√(1+R)/(1-R).We further construct several featured quantities to illustrate it,and formalize the anomalyinduced gravitational wave strain deformation.

关键词： superradiance ultralight boson black hole gravitational wave near-horizon reflection

来源：评论

学校读者我要写书评

暂无评论

Multi-Metric Based Client Selection for Backdoor Defense in Federated Learning 1

Multi-Metric Based Client Selection for Backdoor Defense in ...

引用

1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Yuan, Changjiang Li, Yidong Chen, Naiyue Zhang, Zikai Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing Jiaotong University Ministry of Education China School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781643685779

The distributed nature of federated learning makes it highly susceptible to backdoor attacks, which aim to induce the model to produce incorrect results when specific data is input. Existing defense methods based on distance calculations mostly use one single distance, which can be easily circumvented by backdoor attacks. Therefore, we conducted research on the combination of multiple distances. Based on this, we propose a backdoor defense algorithm based on multi metrics, which evaluates models through distance calculation and normalization to select benign models for aggregation. To verify the effectiveness of our method, we conducted experiments on three datasets, and the results prove that our method can provide good defense without reducing the model's main task accuracy. © 2025 The Authors.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

TCMS: A Multi-Sequence Log Parsing Method Based on Token Conversion

引用

IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 3028-3045页

作者： Wei, Mingkuan Wen, Jigang He, Shiming Xie, Kun Liang, Wei Xie, Gaogang Li, Kenli Zhu, Ziyu Hunan University Ministry of Education Key Laboratory of "Fusion Computing of Supercomputing and Artificial Intelligence" College of Computer Science and Electronics Engineering Changsha410012 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Changsha University of Science and Technology School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha410114 China Beijing100045 China Columbia University Electrical Engineering New YorkNY10027 United States

Detailed system operations are recorded in logs. To ensure system reliability, developers can detect system anomalies through log anomaly detection. Log parsing, which converts semi-structured log messages into structured data, is a crucial step in log anomaly detection and advanced program analysis and verification. Despite the availability of various log parsing tools, they generally suffer from low parsing accuracy and slow efficiency due to the ignorance of variable characteristics and the use of costly pairwise comparison methods. In this paper, we propose a TCMS framework to parse logs, consisting of two main technologies. First, by studying 16 public log datasets, we find that most log variable tokens are structured variable tokens. Based on this discovery, we propose a token conversion algorithm to improve parsing accuracy. This algorithm converts the changed parts in structured variable tokens into wildcards (‘’), preventing these tokens from being directly identified as constant tokens. Second, to improve efficiency, we propose the LogMLCS algorithm, which intelligently constructs a graph to facilitate the extraction of common parts from multiple log messages at once, instead of using pairwise comparisons. Comprehensive experiments conducted on 16 log datasets reveal that our TCMS outperforms seven other parsing methods, achieving the highest parsing accuracy at the fastest speed. Furthermore, experimental results from running a log anomaly detection algorithm in conjunction with different log parsing methods demonstrate that TCMS significantly boosts detection accuracy. For instance, on the OpenStack dataset, our TCMS-facilitated log anomaly detection algorithm achieves a perfect F1-score, precision, and recall of 100% each, surpassing the best peer method by 32.2, 0.8, and 19.5 percentage points, respectively. © 2004-2012 IEEE.

关键词： Accuracy Anomaly Detection Clustering Algorithms Peer To Peer Computing Electronic Mail Source Coding Particle Separators Codes Tokenization Itemsets Log Parsing Log Analysis Token Conversion Log MLCS Log Parsing Pairwise Comparisons Low Accuracy Public datasets Anomaly Detection Common Part Multiple Messages Long Short Term Memory Point Source Target Domain Logarithm Of The Number Captive Animals Multi Objective Optimization Problem Regular Expressions Biological Sequences Rule Based Methods Point In The Graph Kinds Of datasets Template Generation Number Of Templates Similar Template Parse Tree Syntax Errors Harmonic Average Of Precision Log Length Earliest Methods Common Subsequence Precision And Recall Leaf Node Part Of The Message

来源：评论

学校读者我要写书评

暂无评论

Predicting microRNA-Disease Associations Through Multi-View Feature Fusion and Matrix Completion on Graph Convolutional Networks

IEEE Transactions on Computational Biology and Bioinformatic...

引用

IEEE Transactions on Computational Biology and Bioinformatics 2025年第1期22卷 151-159页

作者： Shifei Ding Chuangui Cao Fan Sun Xuan Li Xindong Wu School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of Ministry of Education Xuzhou China Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China

Since the regulatory roles of microRNAs in complex human diseases have been gradually demonstrated, more and more enlightening models were developed for predicting microRNA-disease associations. These models can inform bio-logical studies on the differential expression of microRNAs. In this paper, a graph convolutional network-based model using multi-view feature fusion and matrix completion, FMCGCN for brevity, is proposed. First of all, graph autoencoders are used to learn multiple embeddings from different networks. For convenience, attention mechanisms are used to fuse them. As a result, multi-view features constructed from different perspectives are aggregated. Then, the fused embedding is fed into the graph convolutional network to aggregate local information. This fused embedding is thought to facilitate feature extraction for graph convolutional networks. Finally, feature and nuclear norm minimization, a method for matrix completion, is used to obtain the prediction matrix. Additionally, evaluation results under 5-fold cross-validation prove that FMCGCN outperforms current models in several metrics. Furthermore, case studies for esophageal neoplasms and pancreatic neoplasms also demonstrate the validity of our model.

关键词： Diseases Semantics Predictive models Kernel Topology Graph convolutional networks Feature extraction Learning systems Biological system modeling Reliability

来源：评论

学校读者我要写书评

暂无评论

How to Mitigate Information Loss in knowledge Graphs for GraphRAG: Leveraging Triple Context Restoration and Query-Driven Feedback

arXiv

引用

arXiv 2025年

作者： Huang, Manzong Bu, Chenyang He, Yi Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data [Hefei University of Technology Ministry of Education China School of Data Science William & Mary WilliamsburgVA United States

knowledge Graph (KG)-augmented Large Language Models (LLMs) have recently propelled significant advances in complex reasoning tasks, thanks to their broad domain knowledge and contextual awareness. Unfortunately, current methods often assume KGs to be complete, which is impractical given the inherent limitations of KG construction and the potential loss of contextual cues when converting unstructured text into entity-relation triples. In response, this paper proposes the Triple Context Restoration and Query-driven Feedback (TCR-QF) framework, which reconstructs the textual context underlying each triple to mitigate information loss, while dynamically refining the KG structure by iteratively incorporating query-relevant missing knowledge. Experiments on five benchmark question-answering datasets substantiate the effectiveness of TCR-QF in KG and LLM integration, where it achieves a 29.1% improvement in Exact Match and a 15.5% improvement in F1 over its state-of-the-art GraphRAG competitors. © 2025, CC BY.

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：